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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Adaptive Operator Selection for Meta-Heuristics: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Pei, Jiyuan Mei, Yi Liu, Jialin Zhang, Mengjie Yao, Xin Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington New Zealand Lingnan University School of Data Science Hong Kong Hong Kong

Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. Adaptive selection of suitable operators to the characteristics of different optimization stages is an important task that owns promising potential to improve the performance of a meta-heuristic algorithm. A variety of adaptive operator selection methods have been proposed in last decades, from the machine learning and optimization communities. However, the existing studies have not been systematically reviewed so far. To fill the gap, this paper provides a comprehensive survey of adaptive operator selection for meta-heuristics. According to the information required for selection, adaptive operator selection methods are classified into two categories: (i) stateless methods and (ii) state-based methods. Each category is further summarized into several key components. The strategies of each component belonging to the two categories are reviewed respectively. The motivation, strengths and weaknesses of the proposed strategies are also discussed. Furthermore, studied meta-heuristics and optimization problems in the literature are summarized. The effects from the difference of meta-heuristics and problems to the specific design of methods are discussed, together with the guidance of selecting the suitable method in different application scenarios. At the end, emerging challenges that could guide further research are discussed. © 2020 IEEE.

关键词： Adaptive algorithms

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Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Systems Under DoS Attacks 63

Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Syste...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Wang, Xinhe Wen, Guanghui Zheng, Wei Xing Southeast University School of Cyber Science and Engineering Nanjing211189 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

This paper studies the fixed-time consensus tracking problem of nonlinear multi-agent systems, where communication links are subjected to denial-of-service (DoS) attacks. The DoS attacks make the communication networks switch arbitrarily and therefore the network connectivity may be broken, which poses a serious challenge to the consensus tracking problem within the fixed-time framework. To deal with this issue, a switching-based fixed-time control strategy is proposed. Critical conditions for fixed-time consensus tracking of nonlinear multi-agent systems under DoS attacks are developed. In particular, the adverse impact of DoS attacks on the settling time can be clearly reflected by the fixed-time strategy derived in this paper. Finally, numerical examples are provided to validate the theoretical analysis. © 2024 IEEE.

关键词： Denial-of-service attack

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STFormer: Learning to Explore Spot Relationships for Spatial Transcriptomics Prediction from Histology of Colorectal Cancer 46

STFormer: Learning to Explore Spot Relationships for Spatial...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhan, Gan Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies. © 2024 IEEE.

关键词： WSI circuits

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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

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IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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A New Approach for Iris Segmentation Based on U-Net 6

A New Approach for Iris Segmentation Based on U-Net

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Yang, Yan Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

The concept of iris segmentation was created to increase the accuracy of iris recognition. Past recognition methods used the entire eye image directly for recognition classification, which led to poor recognition results. For the sake of sovling this problem, this paper proposes the ResU-Net (RU-Net) model, which can guide the network to learn more features that distinguish between iris and non-iris pixels. First, based on the U-Net, the backbone network model is changed to ResNet50 in this paper. This has the advantage of reducing the number of parameters and network complexity, and improving the learning capability. For the sake of solving the problem of sample imbalance between iris region and background region, this paper introduces the Focal Loss loss function. focal loss can effectively deal with the case of sample category imbalance and make the network focus more on the pixels that are difficult to classify. In this paper, the proposed RU-Net model is experimentally evaluated on the CASIA-Iris-Thousand dataset. The experimental results demonstrate that RU-Net achieves significant improvements on NIR iris images, reaching 96.22% MIoU and 98.19% MPA. This indicates that the RU-Net method outperforms other representative iris segmentation methods and has better segmentation capability. © 2023 IEEE.

关键词： Pixels

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Prevention of Security Attacks at Wireless Network Layers using Machine Learning Techniques 18

Prevention of Security Attacks at Wireless Network Layers us...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ramisetty, Sowjanya Ghantasala, G. S. Pradeep Gupta, Gaurav Sonia Kg Reddy College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

Wireless sensor networks have great potential for use in flood control, weather forecasting systems, the military, and the healthcare industry. A WSN's nodes are connected to one another and share information. When sending data between nodes, data security is essential. In the world of networking, security is a top priority. data travels along a path when it is transferred from one location to another, increasing the likelihood that a rogue node would enter the network. Detecting malicious nodes within a network presents a formidable challenge, particularly due to external assaults on data packets during their transmission between nodes. Hackers take advantage of weaknesses to manipulate and alter data as it travels from the source node to the destination node. This paper looked at machine learning algorithm-based preventive measures and layer-level security challenges. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine Learning Security Security attacks Wireless Sensor Networks

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Biologically Interpretable Model for Precise Recurrence Prediction of Non-Small Cell Lung Cancer 46

Biologically Interpretable Model for Precise Recurrence Pred...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ai, Yang Du, Xiuju Liu, Jing Li, Yinhao Lin, Lanfen Li, Jingsong Chen, Yen-Wei Ritsumeikan University Graduate School of Information Science and Engineering Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

The precise prediction of preoperative recurrence in non-small cell lung cancer (NSCLC) that is suitable for clinical application is still an open question. Recent advancements integrating genomic data with deep learning have shown promise in enhancing recurrence analysis in NSCLC patients. However, the lack of interpretability in the decision-making process of DNN models has hindered their clinical trustworthiness. In this paper, we propose a novel Biologically Informed Pathway-Aware Neural Network (BioPAN). By automatically extracting biological prior knowledge to guide the architecture of DNN models, we design a unified architecture of gene-pathway-biological process-disease. This approach endows each neuron with entity meaning and learns a multi-level view of biological pathways and processes related to recurrence for fully interpretable NSCLC recurrence prediction. We demonstrated that the proposed model well explains the molecular mechanisms linking genes to NSCLC recurrence, identifies several genes that significantly promote and inhibit recurrence, and elucidates the pivotal roles of various gene pathways at the biological process level. Moreover, it outperforms classical machine learning methods, provides fully interpretable biological information, and requires fewer parameters. Broadly, BioPAN enables clinical discovery and prediction, and may have general applicability across cancer types. © 2024 IEEE.

关键词： Lung cancer

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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